This paper focuses on applying human postures and face tracking technologies to design an autonomous patrol vehicle control system, which contains a wireless video surveillance ability. The entire system includes the ...
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ISBN:
(纸本)9781467384155
This paper focuses on applying human postures and face tracking technologies to design an autonomous patrol vehicle control system, which contains a wireless video surveillance ability. The entire system includes the following several parts: (1) Obtaining the skeleton joints based on the Kinect skeleton tracking: the angles and distances between each human arm's joint are calculated to be the input of an artificial neural networks. (2) Changing the vehicle's gear box: the dynamic gesture recognition is built by using the artificial neural network and a finite state machine. (3) Controlling the vehicle speed, the speed is controlled by a fuzzy control algorithm. (4) Controlling of a motorized camera: the Kinect face tracking function is applied to detect a human's fact direction, so that, the motorized camera's rotation is controlled by the direction. This project expands the application range of intelligent mobile robots and improves the robotic autonomous ability to deal with complex environments.
Recently, a control algorithm has been presented to control the geometry of a prestressed flexible cable net formwork, which is used for the construction of anticlastic thin concrete shell structures in architecture. ...
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ISBN:
(纸本)9781509007561
Recently, a control algorithm has been presented to control the geometry of a prestressed flexible cable net formwork, which is used for the construction of anticlastic thin concrete shell structures in architecture. The most important uncertainties in the model of the cable net are the parameters of unstressed lengths of its edges. As the control algorithm is model-based, its performance depends on the knowledge of these parameters. We present two methods for their identification, which are based on measurements of different configurations of the tensioned cable net. The first identification method uses a change of variables and a linear least squares approach. The second one is based on an optimization problem, which is solved in a distributed way by the Alternating Direction Method of Multipliers (ADMM) and is robust against the expected measurement noise. A numerical example demonstrates the performance of the identification methods.
The paper presents an approach to ontology population as operations with Scott information system. The deducibility relation in the ontology population information system corresponds to rules of input data processing ...
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The paper presents an approach to ontology population as operations with Scott information system. The deducibility relation in the ontology population information system corresponds to rules of input data processing and ontology population. To implement an ontology population process, we suggest a multi-agent approach based on natural language semantic analysis. In the proposed multi-agent model, agents of the following two types interact: information agents corresponding to meaningful units of the information being retrieved and rule agents implementing population rules of the given ontology based on the semantic-syntactic model of the language.
This paper introduces architectural and interaction patterns for integrating crowdsourced human contributions directly into user interfaces. We focus on writing and editing, complex endeavors that span many levels of ...
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This paper introduces architectural and interaction patterns for integrating crowdsourced human contributions directly into user interfaces. We focus on writing and editing, complex endeavors that span many levels of conceptual and pragmatic activity. Authoring tools offer help with pragmatics, but for higher-level help, writers commonly turn to other people. We thus present Soylent, a word processing interface that enables writers to call on Mechanical Turk workers to shorten, proofread, and otherwise edit parts of their documents on demand. To improve worker quality, we introduce the Find-Fix-Verify crowd programming pattern, which splits tasks into a series of generation and review stages. Evaluation studies demonstrate the feasibility of crowdsourced editing and investigate questions of reliability, cost, wait time, and work time for edits.
In order to get the high order evaluation and correlation degree among big data with the characteristics of multidimension and multigranularity, an FCM and NHL based high order mining algorithm driven by big data is p...
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In order to get the high order evaluation and correlation degree among big data with the characteristics of multidimension and multigranularity, an FCM and NHL based high order mining algorithm driven by big data is proposed, which is a kind of machine learning based on qualitative knowledge. The algorithmis applied in scientific and technical talent forecast. Driven by the big data of scientific research track of scientific and technical talents, the index system is designed and the big data is automatically acquired and processed. Accordingly, the high order evaluations in dimension level and target level can be inferred by the correlation weights mining. And the outstanding young talents in material field in 2014 have been actively recommended to review department for decision-making.
The backtracking search optimization algorithm (BSA) is a new nature-inspired method which possesses a memory to take advantage of experiences gained from previous generation to guide the population to the global opti...
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The backtracking search optimization algorithm (BSA) is a new nature-inspired method which possesses a memory to take advantage of experiences gained from previous generation to guide the population to the global optimum. BSA is capable of solving multimodal problems, but it slowly converges and poorly exploits solution. The differential evolution (DE) algorithm is a robust evolutionary algorithm and has a fast convergence speed in the case of exploitive mutation strategies that utilize the information of the best solution found so far. In this paper, we propose a hybrid backtracking search optimization algorithm with differential evolution, called HBD. In HBD, DE with exploitive strategy is used to accelerate the convergence by optimizing one worse individual according to its probability at each iteration process. A suit of 28 benchmark functions are employed to verify the performance of HBD, and the results show the improvement in effectiveness and efficiency of hybridization of BSA and DE.
A maturing and promising technology, Cloud computing can benefit large-scale simulations by providing on-demand, anywhere simulation services to users. In order to enable multitask and multiuser simulation systems wit...
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A maturing and promising technology, Cloud computing can benefit large-scale simulations by providing on-demand, anywhere simulation services to users. In order to enable multitask and multiuser simulation systems with Cloud computing, Cloud simulation platform (CSP) was proposed and developed. To use key techniques of Cloud computing such as virtualization to promote the running efficiency of large-scale military HLA systems, this paper proposes a new type of federate container, virtual machine (VM), and its dynamic migration algorithm considering both computation and communication cost. Experiments show that the migration scheme effectively improves the running efficiency of HLA system when the distributed system is not saturated.
Wind speed/power has received increasing attention around the earth due to its renewable nature as well as environmental friendliness. With the global installed wind power capacity rapidly increasing, wind industry is...
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Wind speed/power has received increasing attention around the earth due to its renewable nature as well as environmental friendliness. With the global installed wind power capacity rapidly increasing, wind industry is growing into a large-scale business. Reliable short-term wind speed forecasts play a practical and crucial role in wind energy conversion systems, such as the dynamic control of wind turbines and power system scheduling. In this paper, an intelligent hybrid model for short-term wind speed prediction is examined;the model is based on cross correlation (CC) analysis and a support vector regression (SVR) model that is coupled with brainstorm optimization (BSO) and cuckoo search (CS) algorithms, which are successfully utilized for parameter determination. The proposed hybrid models were used to forecast short-term wind speeds collected from four wind turbines located on a wind farm in China. The forecasting results demonstrate that the intelligent hybrid models outperform single models for short-term wind speed forecasting, which mainly results from the superiority of BSO and CS for parameter optimization.
In MOPSO (multiobjective particle swarm optimization), to maintain or increase the diversity of the swarm and help an algorithm to jump out of the local optimal solution, PAM (Partitioning Around Medoid) clustering al...
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In MOPSO (multiobjective particle swarm optimization), to maintain or increase the diversity of the swarm and help an algorithm to jump out of the local optimal solution, PAM (Partitioning Around Medoid) clustering algorithm and uniform design are respectively introduced to maintain the diversity of Pareto optimal solutions and the uniformity of the selected Pareto optimal solutions. In this paper, a novel algorithm, the multiobjective particle swarm optimization based on PAM and uniform design, is proposed. The differences between the proposed algorithm and the others lie in that PAM and uniform design are firstly introduced to MOPSO. The experimental results performing on several test problems illustrate that the proposed algorithm is efficient.
This paper studies a production scheduling problem with deteriorating jobs, which frequently arises in contemporary manufacturing environments. The objective is to find an optimal sequence of the set of jobs to minimi...
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This paper studies a production scheduling problem with deteriorating jobs, which frequently arises in contemporary manufacturing environments. The objective is to find an optimal sequence of the set of jobs to minimize the total weighted tardiness, which is an indicator of service quality. The problem belongs to the class of NP-hard. When the number of jobs increases, the computational time required by an optimization algorithm to solve the problem will increase exponentially. To tackle large-scale problems efficiently, a two-stage method is presented in this paper. We partition the set of jobs into a few subsets by applying a neural network approach and thereby transform the large-scale problem into a series of small-scale problems. Then, we employ an improved metaheuristic algorithm (called GTS) which combines genetic algorithm with tabu search to find the solution for each subproblem. Finally, we integrate the obtained sequences for each subset of jobs and produce the final complete solution by enumeration. A fair comparison has been made between the two-stage method and the GTS without decomposition, and the experimental results show that the solution quality of the two-stage method is much better than that of GTS for large-scale problems.
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